Data preparation | Recode and collapse strata | Fit the MAIHDA | Which strata drive the pattern? | How many interactions actually matter? A ROPE | Plot the case study | Results | References
This package is considered a duplicate. The official version of this package is found at:https://hdbt.r-universe.dev/MAIHDA
Package: MAIHDA 0.2.0
Hamid Bulut
MAIHDA: Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy
Tools for Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for intersectional inequality research. Methods are described in Merlo (2018) <doi:10.1016/j.socscimed.2017.12.026> and Evans et al. (2018) <doi:10.1016/j.socscimed.2017.11.011>. The package creates intersectional strata, fits multilevel MAIHDA models, estimates variance partition coefficients, proportional change in variance, stratum effects, and discriminatory-accuracy summaries, and provides diagnostic and presentation plots.
Authors:
MAIHDA_0.2.0.tar.gz
MAIHDA_0.2.0.tar.gz(r-4.7-any)MAIHDA_0.2.0.tar.gz(r-4.6-any)
MAIHDA_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MAIHDA/json (API)
| # Install 'MAIHDA' in R: |
| install.packages('MAIHDA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hdbt/maihda/issues
Pkgdown/docs site:https://hdbt.github.io
Datasets:
- maihda_country_data - Cross-National Educational Achievement Data for MAIHDA
- maihda_health_data - NHANES Health Data Subset for MAIHDA Use
- maihda_long_data - Simulated Longitudinal Data for MAIHDA
- maihda_sim_data - Simulated Health Data for MAIHDA Use
- maihda_sparse_data - Sparse Intersectional Data for Bayesian MAIHDA
Last updated from:572c8deb0a. Checks:4 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 281 | ||
| source / vignettes | OK | 296 | ||
| linux-release-x86_64 | OK | 289 | ||
| wasm-release | OK | 195 |
Exports:calculate_pvccompare_maihdacompare_maihda_groupsfit_maihdaglancemaihdamaihda_aucmaihda_cumulativemaihda_discriminatory_accuracymaihda_icmaihda_interactionsmaihda_mormaihda_tablemaihda_upset_sizemaihda_vpc_responsemake_strataplot_comparisonplot_group_comparisonplot_prediction_deviation_panelspredict_maihdarun_maihda_appstepwise_pcvtheme_maihdatidy
Dependencies:bootclicpp11dplyrfarvergenericsggplot2ggrepelgluegtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixminqanlmenloptrpatchworkpillarpkgconfigpurrrR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Case study: BRFSS mental distress
Last update: 2026-07-02
Started: 2026-07-02
Crossed random effects in MAIHDA: dimensions and contexts
Two different things called "cross-classified" | Part 1, The crossed-dimensions decomposition | Running a crossed-dimensions analysis | Figures | Comparing across a higher-level group | A Bayesian fit | Two important caveats | Part 2, Contextual cross-classified MAIHDA (context =) | With the full maihda() workflow | context = vs. group = | Notes
Last update: 2026-07-02
Started: 2026-06-18
Finding interaction patterns
Overview | Run a standard analysis and choose the multiplicity rule | Highlight flagged strata | Is an interaction negligible? (equivalence / ROPE) | See also | References
Last update: 2026-07-02
Started: 2026-06-18
Interpreting MAIHDA Plots and Diagnostics
Overview | vpc -- variance partition | predicted -- stratum predictions with intervals | obs_vs_shrunken -- shrinkage made visible | effect_decomp -- additive vs. intersection-specific | prediction_deviation -- the deviation dashboard | Group-comparison plots | Customizing the appearance | See also
Last update: 2026-07-02
Started: 2026-06-18
Introduction to MAIHDA
Introduction | Installation | The data | The maihda() workflow | Visual diagnostics | A crossed-dimensions alternative | A contextual cross-classified model | Design-weighted MAIHDA (survey data) | Comparing across groups | Under the hood: the building blocks | Fit a single model | A custom adjusted model and the PCV | Stepwise PCV | Discriminatory accuracy and the response-scale VPC | The group comparison directly | Where to next | Interactive Shiny App | References
Last update: 2026-07-02
Started: 2026-04-03
Bayesian MAIHDA for sparse intersections
Sparse intersectional cells | A dataset with a known interaction | What lme4 reports | What brms adds: posterior uncertainty | Comparison | Why the interval, not the point, is what changes
Last update: 2026-06-18
Started: 2026-06-18
Comparing Intersectional Inequality Across Groups
Introduction | Example data: countries, gender, and socioeconomic status | One-call workflow | Visualizing the comparison | Share versus magnitude | Additive share (PCV) by group | Direct group comparison | Adding bootstrap intervals
Last update: 2026-06-18
Started: 2026-06-18
Interactive Data Analysis with MAIHDA
Introduction | Launching the Application | Online Version | Local Version | App Features | 1. Data Import | 2. Variable Selection & Strata Creation | 3. Model Fitting & Settings | 4. Interactive Visualizations | 5. Stepwise Variance Analysis (PCV) | Summary
Last update: 2026-06-18
Started: 2026-05-16
Longitudinal MAIHDA: intersectional inequalities over time
From a snapshot to a trajectory | The data | Fitting and the time-varying VPC | Decomposing the trajectory: additive vs. multiplicative | Scope and cautions | Reference
Last update: 2026-06-18
Started: 2026-06-18
MAIHDA for Binary Outcomes (Discriminatory Accuracy)
Why binary outcomes? | Fitting a logistic MAIHDA model | The VPC is on the latent scale | Adjusted model and PCV | Discriminatory accuracy (AUC and Median Odds Ratio) | Plots adapt to the binomial family | Count outcomes work the same way | References
Last update: 2026-06-18
Started: 2026-06-18
Planning a MAIHDA analysis
Before you fit | Is MAIHDA the right tool? | The central tradeoff: more dimensions means emptier cells | What sparse cells do: singular fits | Continuous variables and the analytic sample | What the summaries can and cannot tell you | Which engine, which design? | A suggested learning path | References
Last update: 2026-06-18
Started: 2026-06-18
Reporting MAIHDA results: tidy output and publication tables
From a fitted model to a manuscript | glance() -- the one-row headline | tidy() -- estimates as a tidy tibble | maihda_table() -- the two canonical write-up tables | Choosing a model structure with maihda_ic() | See also | References
Last update: 2026-06-18
Started: 2026-06-18
